ishimura a, shimizu y, rahimzadeh-bajgiran p, omasa k (2011). remote sensing of japanese beech...

vol. 4, pp. 195-199 (Nov 2011)vol. 4, pp. 195-199 (Nov 2011)

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Copyright © 2011 by the Italian Society of Silviculture and Forest EcologyCopyright © 2011 by the Italian Society of Silviculture and Forest Ecology

doi: 10.3832/ifor0592-004doi: 10.3832/ifor0592-004

Collection: Collection: COST Action FP0903 (2010) - Rome (Italy)COST Action FP0903 (2010) - Rome (Italy)

“Research, monitoring and modelling in the study of climate change and air pollution impacts on forest ecosystems”“Research, monitoring and modelling in the study of climate change and air pollution impacts on forest ecosystems”

Guest Editors: E Paoletti, J-P Tuovinen, N Clarke, G Matteucci, R Matyssek, G Wieser, R Fischer, P Cudlin, N PotocicGuest Editors: E Paoletti, J-P Tuovinen, N Clarke, G Matteucci, R Matyssek, G Wieser, R Fischer, P Cudlin, N Potocic

RESEARCH ARTICLESRESEARCH ARTICLES

A. IshimuraA. Ishimura, , Y. ShimizuY. Shimizu, , P. Rahimzadeh-BajgiranP. Rahimzadeh-Bajgiran, , K. OmasaK. Omasa

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Surveys and monitoring of plant responses to environmental stress are important for us to understand plant functions and preserveSurveys and monitoring of plant responses to environmental stress are important for us to understand plant functions and preserve

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Ishimura A, Shimizu Y, Rahimzadeh-Bajgiran P, Omasa K (2011). Remote... http://www.sisef.it/iforest/contents/?id=ifor0592-004

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remote-sensing data to estimate vegetation conditions, Omasa et al. (remote-sensing data to estimate vegetation conditions, Omasa et al. ([7]) and Omasa () and Omasa ([6]) examined the effect of temperature) examined the effect of temperature

differences on transpiration rate and stomatal conductance in areas near urban environments affected by air pollution.differences on transpiration rate and stomatal conductance in areas near urban environments affected by air pollution.

Rahimzadeh-Bajgiran et al. (Rahimzadeh-Bajgiran et al. ([9]) used MOderate Resolution Imaging Spectroradiometer (MODIS) satellite data to examine drought) used MOderate Resolution Imaging Spectroradiometer (MODIS) satellite data to examine drought

in semi-arid regions. The spatial resolution of MODIS is only moderate (250, 500, or 1000 m), but it can observe land surfacesin semi-arid regions. The spatial resolution of MODIS is only moderate (250, 500, or 1000 m), but it can observe land surfaces

frequently and over wide areas. Thus, MODIS data are useful for studying and analyzing wide ranges of land cover and time seriesfrequently and over wide areas. Thus, MODIS data are useful for studying and analyzing wide ranges of land cover and time series

changes.changes.

These studies focused not only on land cover classification, but also analyzing information on the physiological and ecologicalThese studies focused not only on land cover classification, but also analyzing information on the physiological and ecological

aspects of vegetation by calculating indices correlated with vegetation density or evapotranspiration rate. For example, theaspects of vegetation by calculating indices correlated with vegetation density or evapotranspiration rate. For example, the

Normalized Difference Vegetation Index (NDVI - Normalized Difference Vegetation Index (NDVI - [11]) evaluates the density of vegetation. The Temperature Vegetation Dryness) evaluates the density of vegetation. The Temperature Vegetation Dryness

Index (TVDI - Index (TVDI - [12]) and the improved Temperature Vegetation Dryness Index (iTVDI - ) and the improved Temperature Vegetation Dryness Index (iTVDI - [10]) evaluate the evapotranspiration rate.) evaluate the evapotranspiration rate.

TVDI is calculated by using a vegetation index (TVDI is calculated by using a vegetation index (e.g.e.g., the NDVI) and the surface temperature. In contrast, iTVDI is calculated by, the NDVI) and the surface temperature. In contrast, iTVDI is calculated by

using a vegetation index and the difference between surface and air temperatures. In fully covered vegetation areas in which theusing a vegetation index and the difference between surface and air temperatures. In fully covered vegetation areas in which the

leaf surface is not wet, TVDI and iTVDI can be used to evaluate differences in the rates of evapotranspiration over areas of theleaf surface is not wet, TVDI and iTVDI can be used to evaluate differences in the rates of evapotranspiration over areas of the

same vegetation density. iTVDI corrects for the effect of elevation on surface temperature by using air temperature. In the Tanzawasame vegetation density. iTVDI corrects for the effect of elevation on surface temperature by using air temperature. In the Tanzawa

Mountains, which were the focus of this study, the existence of dead trees and trees with discolored leaves and low biomass hasMountains, which were the focus of this study, the existence of dead trees and trees with discolored leaves and low biomass has

been reported (been reported ([13]). By using iTVDI, we can expect to detect trees that have normal quantities of leaves but depressed). By using iTVDI, we can expect to detect trees that have normal quantities of leaves but depressed

evapotranspiration rates because of stomatal closure caused by lack of water or air pollution. To detect the ongoing forest declineevapotranspiration rates because of stomatal closure caused by lack of water or air pollution. To detect the ongoing forest decline

(([14]) that is occurring in the Tanzawa Mountains, we need to evaluate such information on the physiological and ecological) that is occurring in the Tanzawa Mountains, we need to evaluate such information on the physiological and ecological

condition of vegetation.condition of vegetation.

Here, we made NDVI, TVDI, and iTVDI maps from MODIS data retrieved frequently in 2007 and compared them with an existingHere, we made NDVI, TVDI, and iTVDI maps from MODIS data retrieved frequently in 2007 and compared them with an existing

mortality map of beech forests in the study area (mortality map of beech forests in the study area ([3]). We examined the possibility of estimating the beech forest mortality rate and). We examined the possibility of estimating the beech forest mortality rate and

differences in evapotranspiration related to stomatal closure.differences in evapotranspiration related to stomatal closure.

The study area covered part of the Tanzawa Mountains of Japan (The study area covered part of the Tanzawa Mountains of Japan (Fig. 1 - about 6300 km - about 6300 km22: 138.62-139.40°E, 35.33-36.00 °N). The: 138.62-139.40°E, 35.33-36.00 °N). The

Tanzawa Mountains include part of Kanagawa, Yamanashi and Shizuoka prefectures. Natural beech forests are preserved in theTanzawa Mountains include part of Kanagawa, Yamanashi and Shizuoka prefectures. Natural beech forests are preserved in the

areas, which has been designated a Quasi-National Park. However, decline of the beech forests has become a serious problemareas, which has been designated a Quasi-National Park. However, decline of the beech forests has become a serious problem

since the 1980s and continues to spread. Koshiji et al. (since the 1980s and continues to spread. Koshiji et al. ([3]) reported that the extent of forest containing more than 201 dead trees) reported that the extent of forest containing more than 201 dead trees

per 1 kmper 1 km22 was about 55 km was about 55 km22 in 1985. Air pollution (mainly from ozone), water stress, and insect infestation are thought to be factors in 1985. Air pollution (mainly from ozone), water stress, and insect infestation are thought to be factors

in this decline, but the main factors have not yet been disclosed yet and the mechanism of the decline remains unknown (in this decline, but the main factors have not yet been disclosed yet and the mechanism of the decline remains unknown ([14]). Our). Our

study area included Tanzawa National Park where most of the decline has been reported (study area included Tanzawa National Park where most of the decline has been reported (Fig. 1).).

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We used remote-sensing data obtained from the MODIS sensor aboard the Terra satellite. MODIS has 36 spectral bandsWe used remote-sensing data obtained from the MODIS sensor aboard the Terra satellite. MODIS has 36 spectral bands

(wavelength ranging from 0.40 to 14.4 μm) and supplies many kinds of data products. We used MOD09A1 data, which provide(wavelength ranging from 0.40 to 14.4 μm) and supplies many kinds of data products. We used MOD09A1 data, which provide

images of reflectance (8 bands, 459 to 2155 nm, with 500 m pixel resolution) and MOD11A2 data, which provide images of landimages of reflectance (8 bands, 459 to 2155 nm, with 500 m pixel resolution) and MOD11A2 data, which provide images of land

surface temperature (with 1000 m pixel resolution). Each dataset is a composite of 8 days’ data to reduce the effects of clouds andsurface temperature (with 1000 m pixel resolution). Each dataset is a composite of 8 days’ data to reduce the effects of clouds and

noise. We used 46 data sets from 2007. In the study area, the number of pixels (with 500 m pixel resolution) was 24 820.noise. We used 46 data sets from 2007. In the study area, the number of pixels (with 500 m pixel resolution) was 24 820.

We used daily air temperature and elevation data observed at 10 meteorological stations (Automated Meteorological DataWe used daily air temperature and elevation data observed at 10 meteorological stations (Automated Meteorological Data

Acquisition System - AMeDAS) in the study area and a Global Digital Elevation Model (GDEM - with 30 m pixel resolution) obtainedAcquisition System - AMeDAS) in the study area and a Global Digital Elevation Model (GDEM - with 30 m pixel resolution) obtained

from the ASTER sensor abroad the Terra to produce air temperature maps.from the ASTER sensor abroad the Terra to produce air temperature maps.

The methodology is illustrated in the flow chart in The methodology is illustrated in the flow chart in Fig. 2. We made maps of NDVI, TVDI and iTVDI and compared them with a forest. We made maps of NDVI, TVDI and iTVDI and compared them with a forest

mortality map (mortality map ([3]). For image processing, we reprojected these remote-sensing images and masked them to reduce the effect of). For image processing, we reprojected these remote-sensing images and masked them to reduce the effect of

cloud and noise. The mortality map showed the number of dead trees per 1 kmcloud and noise. The mortality map showed the number of dead trees per 1 km22. Koshiji et al. (. Koshiji et al. ([3]) used aerial photography (with) used aerial photography (with

25 cm pixel resolution) from 1985 and counted the number of dead trees by visual observation. The extent of the mortality was25 cm pixel resolution) from 1985 and counted the number of dead trees by visual observation. The extent of the mortality was

classified into three levels, low, moderate, or high. Low means that there were fewer than 201 dead trees per 1 kmclassified into three levels, low, moderate, or high. Low means that there were fewer than 201 dead trees per 1 km22, moderate, moderate

means 201 to 400, and high means more than 401. Yamane et al. (means 201 to 400, and high means more than 401. Yamane et al. ([13]) conducted a ground survey in the Tanzawa Mountains in) conducted a ground survey in the Tanzawa Mountains in

2007 and suggested that the area where there had been more dead trees in 1985 represented stronger decline in 2007. Therefore,2007 and suggested that the area where there had been more dead trees in 1985 represented stronger decline in 2007. Therefore,

we used the 1985 mortality map as ground truth data for 2007.we used the 1985 mortality map as ground truth data for 2007.

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NDVI, which uses the reflectance in the near-infrared and red bands, is a popular index for vegetation monitoring. NDVI reflectsNDVI, which uses the reflectance in the near-infrared and red bands, is a popular index for vegetation monitoring. NDVI reflects

both chlorophyll content and vegetation amount. MODIS observes these wavelengths as band 2 (841 to 876 nm - near infrared)both chlorophyll content and vegetation amount. MODIS observes these wavelengths as band 2 (841 to 876 nm - near infrared)

and band 1 (620 to 670 nm - red). NDVI is calculated as follows (and band 1 (620 to 670 nm - red). NDVI is calculated as follows (eqn. 1):):

where where REDRED and and NIRNIR represent the spectral reflectance measurements acquired in the red and near-infrared regions, respectively. represent the spectral reflectance measurements acquired in the red and near-infrared regions, respectively.

Generally, surface temperature (Ts) and surface temperature minus air temperature (Ts - Ta) reflect evapotranspiration rates,Generally, surface temperature (Ts) and surface temperature minus air temperature (Ts - Ta) reflect evapotranspiration rates,

because the Ts value is reduced by latent heat. Therefore, we can use Ts and (Ts - Ta) as indices of the evapotranspiration rates.because the Ts value is reduced by latent heat. Therefore, we can use Ts and (Ts - Ta) as indices of the evapotranspiration rates.

(Ts - Ta) is the surface temperature corrected by Ta. We consider that this value works well in mountainous areas, where the effect(Ts - Ta) is the surface temperature corrected by Ta. We consider that this value works well in mountainous areas, where the effect

of elevation on Ts cannot be ignored.of elevation on Ts cannot be ignored.

We made (Ts - Ta) maps to produce iTVDI maps, calculating the differences between the Ts and Ta maps. We obtained Ts mapsWe made (Ts - Ta) maps to produce iTVDI maps, calculating the differences between the Ts and Ta maps. We obtained Ts maps

from MODIS data and made the Ta maps from AMeDAS data and the GDEM. We calculated the daily air temperature value at eachfrom MODIS data and made the Ta maps from AMeDAS data and the GDEM. We calculated the daily air temperature value at each

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Ta) map scenes and calculated their differences between them.Ta) map scenes and calculated their differences between them.

TVDI and iTVDI are indices developed to estimate the evapotranspiration per each vegetation amount. These indices wereTVDI and iTVDI are indices developed to estimate the evapotranspiration per each vegetation amount. These indices were

developed from the water deficit index (WDI - developed from the water deficit index (WDI - [5]). WDI quantifies the rate of evapotranspiration from partial vegetation cover. In a). WDI quantifies the rate of evapotranspiration from partial vegetation cover. In a

vegetation environment where the potential maximum evapotranspiration rate is LEvegetation environment where the potential maximum evapotranspiration rate is LEpp and the actual one is LE, WDI is calculated by and the actual one is LE, WDI is calculated by

using using eqn. 2. When the evapotranspiration rate is relatively small, the WDI value approaches 1 (. When the evapotranspiration rate is relatively small, the WDI value approaches 1 (eqn. 2):):

where (Ts - Ta)where (Ts - Ta)obsobs is the surface temperature minus air temperature at the observation time, and (Ts - Ta) is the surface temperature minus air temperature at the observation time, and (Ts - Ta)wetwet and (Ts - Ta) and (Ts - Ta)drydry are are

(respectively) the minimum and maximum (Ts - Ta) for the same vegetation index value ((respectively) the minimum and maximum (Ts - Ta) for the same vegetation index value (e.g.e.g., NDVI) in the trapezoidal shape of the, NDVI) in the trapezoidal shape of the

vegetation index vegetation index vs.vs. (Ts - Ta). WDI needs calibration using Penman-Monteith combined with energy balance equation ( (Ts - Ta). WDI needs calibration using Penman-Monteith combined with energy balance equation ([4]).).

Therefore, in the WDI model, some of the inputs required to estimate the vertices of the trapezoid are difficult to assess (Therefore, in the WDI model, some of the inputs required to estimate the vertices of the trapezoid are difficult to assess (e.g.e.g.,,

aerodynamic resistance, maximum and minimum canopy resistance, and soil heat flux).aerodynamic resistance, maximum and minimum canopy resistance, and soil heat flux).

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retrieved by using satellite and meteorological data and by calculating the positions of two lines (retrieved by using satellite and meteorological data and by calculating the positions of two lines (Fig. 3, segments 1-2 and 3-4) that, segments 1-2 and 3-4) that

define the top and bottom of the plots of the Ts define the top and bottom of the plots of the Ts vs.vs. NDVI space (for TVDI) and the (Ts - Ta) NDVI space (for TVDI) and the (Ts - Ta) vs.vs. NDVI space (for iTVDI). The NDVI space (for iTVDI). The

theoretical shape resulting from (Ts - Ta) theoretical shape resulting from (Ts - Ta) vs.vs. NDVI that is used to derive iTVDI is presented in NDVI that is used to derive iTVDI is presented in Fig. 3. The . The nn vertices ( vertices (nn = 1, 2, 3, 4) = 1, 2, 3, 4)

in in Fig. 3 indicate well-watered full-cover vegetation, full-cover vegetation suffering high water stress, saturated bare soil, and dry indicate well-watered full-cover vegetation, full-cover vegetation suffering high water stress, saturated bare soil, and dry

bare soil, respectively. Both TVDI and iTVDI indicate the relative position of Ts or (Ts - Ta) on a line on which NDVI remainsbare soil, respectively. Both TVDI and iTVDI indicate the relative position of Ts or (Ts - Ta) on a line on which NDVI remains

constant. iTVDI at point C in constant. iTVDI at point C in Fig. 3 is calculated by using segments AB and BC as in is calculated by using segments AB and BC as in eqn. 3 and TVDI is calculated by using the and TVDI is calculated by using the

same principle as in same principle as in eqn. 4 ( (eqn. 3, , eqn. 4):):

We selected a study area that included many characteristic environments, including forest, soil, and cropland. We positioned theWe selected a study area that included many characteristic environments, including forest, soil, and cropland. We positioned the

right and left lines of the trapezoid to include 95% of all plots (mean of NDVI ± 2 standard deviations [SD]) and the top and bottomright and left lines of the trapezoid to include 95% of all plots (mean of NDVI ± 2 standard deviations [SD]) and the top and bottom

lines to delineate 10 plots including 99% of all the original study plots (mean of (Ts - Ta) ± 3 SD) at each step (by dividing thelines to delineate 10 plots including 99% of all the original study plots (mean of (Ts - Ta) ± 3 SD) at each step (by dividing the

difference between the right and left edges into 10 parts - difference between the right and left edges into 10 parts - Fig. 3).).

Both TVDI and iTVDI vary between 0 and 1. Values increased in environments where evapotranspiration rates are low. TheseBoth TVDI and iTVDI vary between 0 and 1. Values increased in environments where evapotranspiration rates are low. These

values can be used to estimate differences in evapotranspiration rates between areas with the same vegetation amount. Invalues can be used to estimate differences in evapotranspiration rates between areas with the same vegetation amount. In

mountainous areas such as the Tanzawa Mountains, environmental lapse rate correction should work well on iTVDI.mountainous areas such as the Tanzawa Mountains, environmental lapse rate correction should work well on iTVDI.

We made Ta maps from the data from the 10 AMeDAS stations and the GDEM data (example for the period July 28 to August 4,We made Ta maps from the data from the 10 AMeDAS stations and the GDEM data (example for the period July 28 to August 4,

2007 - 2007 - Fig. 4). To verify the accuracy of the method, we made maps by using the data from nine of the stations and compared the). To verify the accuracy of the method, we made maps by using the data from nine of the stations and compared the

estimated data with the observations at the 10th, excluded, station. The method calibrated air temperature with good accuracyestimated data with the observations at the 10th, excluded, station. The method calibrated air temperature with good accuracy

(RMSE = 0.49 °C).(RMSE = 0.49 °C).

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We focused on two temporal datasets, spring (May 1 to May 8, 2007), when the leaves emerge, and summer (July 28 to August 4,We focused on two temporal datasets, spring (May 1 to May 8, 2007), when the leaves emerge, and summer (July 28 to August 4,

2007) when the amount of leaf cover peaks. We show NDVI, TVDI, and iTVDI maps for summer (2007) when the amount of leaf cover peaks. We show NDVI, TVDI, and iTVDI maps for summer (Fig. 5). The black pixels are areas). The black pixels are areas

where there are no data because of cloud masking. The NDVI values at almost all pixels were greater than 0.8. This suggestedwhere there are no data because of cloud masking. The NDVI values at almost all pixels were greater than 0.8. This suggested

that the NDVI was unable to detect dead trees. In contrast, the TVDI and iTVDI maps had spatially varied values. The finding thatthat the NDVI was unable to detect dead trees. In contrast, the TVDI and iTVDI maps had spatially varied values. The finding that

the NDVI map had uniform values suggested that there were areas where the evapotranspiration rates were different despite thethe NDVI map had uniform values suggested that there were areas where the evapotranspiration rates were different despite the

same vegetation amounts.same vegetation amounts.

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We verified the NDVI, TVDI, and iTVDI values at the pixels as Low, Moderate, or High on the beech forest mortality map (We verified the NDVI, TVDI, and iTVDI values at the pixels as Low, Moderate, or High on the beech forest mortality map ([3] - - Fig.

6). The data for both spring and summer showed that iTVDI had a better relationship with mortality rate than did NDVI or TVDI. As). The data for both spring and summer showed that iTVDI had a better relationship with mortality rate than did NDVI or TVDI. As

was found in the comparison of summer data among the three indices, NDVI values in summer were so high (near 0.9) that NDVIwas found in the comparison of summer data among the three indices, NDVI values in summer were so high (near 0.9) that NDVI

did not detect dead trees. TVDI values were unreliable in summer, because areas with low evapotranspiration rates should havedid not detect dead trees. TVDI values were unreliable in summer, because areas with low evapotranspiration rates should have

had relatively low values.had relatively low values.

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detects differences in evapotranspiration in areas with the same vegetation amount. Thus, iTVDI detected well-covered areasdetects differences in evapotranspiration in areas with the same vegetation amount. Thus, iTVDI detected well-covered areas

where transpiration rates were low; these areas tended to have dead trees. The difference between TVDI and iTVDI showed thatwhere transpiration rates were low; these areas tended to have dead trees. The difference between TVDI and iTVDI showed that

the effect of an elevation in Ts could not be ignored. Reduction in transpiration rates is likely attributable to stomatal closure causedthe effect of an elevation in Ts could not be ignored. Reduction in transpiration rates is likely attributable to stomatal closure caused

by air pollution and water stress. iTVDI suggested the existence of areas on the map where transpiration rates were reduced byby air pollution and water stress. iTVDI suggested the existence of areas on the map where transpiration rates were reduced by

stomatal closure. Judging from the above, we found that indices calculated from the MODIS data and the meteorological data,stomatal closure. Judging from the above, we found that indices calculated from the MODIS data and the meteorological data,

especially iTVDI, could be used to estimate forest mortality rate in both spring and summer. iTVDI can thus be used to detect areasespecially iTVDI, could be used to estimate forest mortality rate in both spring and summer. iTVDI can thus be used to detect areas

that are suffering a reduction in transpiration rates and are at risk of future decline. It should therefore be useful as an index forthat are suffering a reduction in transpiration rates and are at risk of future decline. It should therefore be useful as an index for

monitoring vegetation decline. Comparison with other remote-sensing studies of forests reveals that vegetation indices tend to bemonitoring vegetation decline. Comparison with other remote-sensing studies of forests reveals that vegetation indices tend to be

saturated in the high biomass region, and there has been hope for a vegetation index that sensitively reflects the vegetation statussaturated in the high biomass region, and there has been hope for a vegetation index that sensitively reflects the vegetation status

in such regions (in such regions ([1]). We found that iTVDI worked well in high biomass regions such as those in the Tanzawa Mountains.). We found that iTVDI worked well in high biomass regions such as those in the Tanzawa Mountains.

Diagnosis and monitoring of plant responses to environmental stress are important to our understanding and preservation of plantDiagnosis and monitoring of plant responses to environmental stress are important to our understanding and preservation of plant

function. We tried to detect forest mortality rate in the Tanzawa Mountains by calculating NDVI, TVDI and iTVDI maps using MODISfunction. We tried to detect forest mortality rate in the Tanzawa Mountains by calculating NDVI, TVDI and iTVDI maps using MODIS

data. NDVI was saturated and TVDI was unreliable in summer. In contrast, iTVDI worked well in both spring and summer anddata. NDVI was saturated and TVDI was unreliable in summer. In contrast, iTVDI worked well in both spring and summer and

showed a good relationship with forest mortality rate.showed a good relationship with forest mortality rate.

iTVDI detects areas where evapotranspiration rates are relatively reduced and the robustness of this index makes it useful foriTVDI detects areas where evapotranspiration rates are relatively reduced and the robustness of this index makes it useful for

monitoring mountainous areas. We therefore expect that it will be a good index for monitoring vegetation decline.monitoring mountainous areas. We therefore expect that it will be a good index for monitoring vegetation decline.

Huete A, Didan K, Miura T, Rodriguez E, Gao X, Ferreira L (2002). Overview of the radiometric and biophysical performanceHuete A, Didan K, Miura T, Rodriguez E, Gao X, Ferreira L (2002). Overview of the radiometric and biophysical performance

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Omasa K (2002). Diagnosis of stomatal response and gas exchange of trees by thermal remote sensing. In: “Air pollution andOmasa K (2002). Diagnosis of stomatal response and gas exchange of trees by thermal remote sensing. In: “Air pollution and

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